From 14f57a514ce3e92828f894a7e5f393a921f01024 Mon Sep 17 00:00:00 2001 From: lanusStats <101477588+lanusStats@users.noreply.github.com> Date: Wed, 16 Aug 2023 20:10:38 -0300 Subject: [PATCH 1/4] add match momentum example --- examples/tutorials/plot_match_momentum.py | 106 ++++++++++++++++++++++ 1 file changed, 106 insertions(+) create mode 100644 examples/tutorials/plot_match_momentum.py diff --git a/examples/tutorials/plot_match_momentum.py b/examples/tutorials/plot_match_momentum.py new file mode 100644 index 0000000..95e7b15 --- /dev/null +++ b/examples/tutorials/plot_match_momentum.py @@ -0,0 +1,106 @@ +""" +=============== +Match Momentum +=============== + +This example shows how to plot match momentum using the calculations of expected threat (xT) model shown in the same folder. +It can be applied to any match where we have the xT values of every event that happend, the team that made it and the minute when it happened. + +""" + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from scipy.interpolate import make_interp_spline + +############################################################################## +# Import the data +# --------------- +# For this, we're using data from the FA Women's Super League 2019/20 as used in the `Improvements to expected threat ` +# but just limiting to one match with the xT already calculated (dataframe move_success). +# The only thing that changes is the fact that we need the columns of "minute" and "team_name" to plot match momentum correctly. +# You may add them where the list "cols" is defined in the other tutorial. + +match_events = pd.read_csv('xt_match_example.csv') + +############################################################################## +# Calculating the moving averages of Expected Threat +# -------------- +# Here we need to know the threat that every team had in each minute of the match +# For that, we need to split the match_events dataframe in two separate ones (one for each team) and group the sum of xT values +# by minutes. + +teams = match_events.team_name.unique() +team1, team2 = teams[0], teams[1] + +team1_actions = match_events[match_events['team_name'] == team1].groupby('minute').sum() +team2_actions = match_events[match_events['team_name'] == team2].groupby('minute').sum() + +# With the two dataframes separated, we need to calculate the mean of the moving average of the Expected Threat. +# We are using five as a parameter to take into consideration the last five minutes of the match. + +team1_actions['rolling_xt'] = team1_actions.xt.rolling(5).mean() +team2_actions['rolling_xt'] = team2_actions.xt.rolling(5).mean() + +# Then we need to merge them by the minute so we can calculate the difference between the two values +# which is going to give us the values of the y-axis in the match momentum plot. + +match_xt = team1_actions.merge(team2_actions, left_index=True, right_index=True, how='outer', suffixes=['_t1', '_t2']) +match_xt = match_xt[['rolling_xt_t1', 'rolling_xt_t2']].fillna(0) #so the difference can be calculated correctly. +match_xt['total_diff'] = match_xt['rolling_xt_t1'] - match_xt['rolling_xt_t2'] + +############################################################################## +# Plot Match Momentum +# -------------- +# The only things we need from the events are the minutes (x-axis) and the difference between the moving averages of xT for both teams (y-axis) +# Also, is needed that we interpolate the values. + +fig, ax = plt.subplots(figsize=(16,9)) +fig.set_facecolor('white') + +local_color = 'lightblue' +visit_color = 'red' + +x = match_xt.index +y = match_xt.total_diff + +X_Y_Spline = make_interp_spline(x, y) +X_ = np.linspace(x.min(), x.max(), 500) +Y_ = X_Y_Spline(X_) + +ax.plot(X_, Y_) + +# Then all esthetic functions for the plot to be more readable and clear + +ax.axhline(0) # To separate the values + +# To fill with one color for the positive and negative values we use this function + +ax.fill_between(X_, Y_, color=local_color, where= Y_ > 0) +ax.fill_between(X_, Y_, color=visit_color, where= Y_ < 0) + +ax.set_xlim(0,match_xt.index[-1]) #To compact the plot in the x-axis +ax.spines[['right','left','top']].set_visible(False) + +# Considerations in x and y axis. +ax.set_xlabel('Minutes', fontsize=20) +ax.set_yticks([]) #You can comment this line if you want the difference of values to be shown +ax.set_xticks(range(0,match_xt.index[-1], 10)) + +# Lastly, if we have the data of goals in out events we can make to lists: one for the minutes and another with which team scored. +# With that we can plot a line showing every single one of them + +min_goals = [ 6, 9, 16, 22, 69] +team_goals = ['Reading WFC', 'Everton LFC', 'Reading WFC', 'Everton LFC', 'Reading WFC'] + + +for goal in range(len(min_goals)): + if team_goals[goal] == team1: + ax.axvline(min_goals[goal], lw=2, color=local_color, ls=':') + else: + ax.axvline(min_goals[goal], lw=2, color=visit_color, ls=':') + +#And a title using the data of the team_name columns +ax.set_title(f'Match momentum {team1} vs. {team2}', fontsize=26) + +plt.show() \ No newline at end of file From 9f711c844d1169ee5ab7f6f6807e6208d18fa027 Mon Sep 17 00:00:00 2001 From: lanusStats <101477588+lanusStats@users.noreply.github.com> Date: Wed, 16 Aug 2023 20:39:55 -0300 Subject: [PATCH 2/4] add githubusercontent link to csv file --- examples/tutorials/plot_match_momentum.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/tutorials/plot_match_momentum.py b/examples/tutorials/plot_match_momentum.py index 95e7b15..7c36374 100644 --- a/examples/tutorials/plot_match_momentum.py +++ b/examples/tutorials/plot_match_momentum.py @@ -21,7 +21,7 @@ # The only thing that changes is the fact that we need the columns of "minute" and "team_name" to plot match momentum correctly. # You may add them where the list "cols" is defined in the other tutorial. -match_events = pd.read_csv('xt_match_example.csv') +match_events = pd.read_csv('https://raw.githubusercontent.com/LanuStats/futbol-data-visualización/archivos/streams-videos/archivos/xt_match_example.csv') ############################################################################## # Calculating the moving averages of Expected Threat From aec8c3a11a8223f0471374eafb3b0aabb43a01e0 Mon Sep 17 00:00:00 2001 From: lanusStats <101477588+lanusStats@users.noreply.github.com> Date: Wed, 16 Aug 2023 20:57:52 -0300 Subject: [PATCH 3/4] link typo --- examples/tutorials/plot_match_momentum.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/tutorials/plot_match_momentum.py b/examples/tutorials/plot_match_momentum.py index 7c36374..e58974d 100644 --- a/examples/tutorials/plot_match_momentum.py +++ b/examples/tutorials/plot_match_momentum.py @@ -21,7 +21,7 @@ # The only thing that changes is the fact that we need the columns of "minute" and "team_name" to plot match momentum correctly. # You may add them where the list "cols" is defined in the other tutorial. -match_events = pd.read_csv('https://raw.githubusercontent.com/LanuStats/futbol-data-visualización/archivos/streams-videos/archivos/xt_match_example.csv') +match_events = pd.read_csv("https://raw.githubusercontent.com/lanusStats/futbol-data-visualizacion/main/archivos/streams-videos/archivos/xt_match_example.csv") ############################################################################## # Calculating the moving averages of Expected Threat From 9fc94772897b02491afe645ab1e9f19270a259be Mon Sep 17 00:00:00 2001 From: lanusStats <101477588+lanusStats@users.noreply.github.com> Date: Mon, 21 Aug 2023 14:16:55 -0300 Subject: [PATCH 4/4] changing to the mplsoccer-assets csv link --- examples/tutorials/plot_match_momentum.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/tutorials/plot_match_momentum.py b/examples/tutorials/plot_match_momentum.py index e58974d..9f40712 100644 --- a/examples/tutorials/plot_match_momentum.py +++ b/examples/tutorials/plot_match_momentum.py @@ -21,7 +21,7 @@ # The only thing that changes is the fact that we need the columns of "minute" and "team_name" to plot match momentum correctly. # You may add them where the list "cols" is defined in the other tutorial. -match_events = pd.read_csv("https://raw.githubusercontent.com/lanusStats/futbol-data-visualizacion/main/archivos/streams-videos/archivos/xt_match_example.csv") +match_events = pd.read_csv("https://raw.githubusercontent.com/andrewRowlinson/mplsoccer-assets/main/xt_match_example.csv") ############################################################################## # Calculating the moving averages of Expected Threat